Learning based primary user activity prediction in cognitive radio networks for efficient dynamic spectrum access

Efficient spectrum sensing can be realized by predicting the future idle times of primary users' activity in a cognitive radio network. In dynamic spectrum access, based on a reliable prediction scheme, a secondary user chooses a channel with the longest idle time for data transmission. In this paper, four supervised machine learning techniques, two from ANN, i.e. Multilayer Perceptron & Recurrent Neural Networks, and two from Support Vector Machines (SVM), i.e. SVM with Linear Kernel, SVM with Gaussian Kernel, have been employed to investigate the prediction of primary activity. Poisson, Interrupted Poisson and Self-similar traffics are used for the analysis of licensed user environment. Data generated by each traffic distribution is used in the training phase individually with the help of each learning model after which, the testing is done for the primary activity prediction. The results highlight the analysis of the learning techniques in accordance with various traffic statistics, and suggest the best learning model for accurate primary user activity prediction.